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Optimization of fault diagnosis based on the combination of Bayesian Networks and case Based Reasoning

L. Bennacer L. Ciavaglia A. Chibani 1 Yacine Amirat 1 A. Mellouk 2
1 SIRIUS
LISSI - Laboratoire Images, Signaux et Systèmes Intelligents
2 CIR
LISSI - Laboratoire Images, Signaux et Systèmes Intelligents
Abstract : Fault diagnosis is one of the most important tasks in fault management. The main objective of the fault management system is to detect and localize failures as soon as they occur to minimize their effects on the network performance and therefore on the service quality perceived by users. In this paper, we present a new hybrid approach that combines Bayesian Networks and Case-Based Reasoning to overcome the usual limits of fault diagnosis techniques and reduce human intervention in this process. The proposed mechanism allows identifying the root cause failure with a finer precision and high reliability while reducing the process computation time and taking into account the network dynamicity.
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https://hal.archives-ouvertes.fr/hal-01678616
Contributor : Yacine Amirat <>
Submitted on : Tuesday, January 9, 2018 - 11:52:05 AM
Last modification on : Friday, October 4, 2019 - 1:32:18 AM

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  • HAL Id : hal-01678616, version 1

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L. Bennacer, L. Ciavaglia, A. Chibani, Yacine Amirat, A. Mellouk. Optimization of fault diagnosis based on the combination of Bayesian Networks and case Based Reasoning. Proc. Of the IEEE/IFIP Network Operations and Management Symposium (NOMS), Apr 2012, Hawaii, USA, United States. pp.619-622. ⟨hal-01678616⟩

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